Improved Quantum Boosting

Authors Adam Izdebski, Ronald de Wolf

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Adam Izdebski
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
Ronald de Wolf
  • QuSoft, CWI and University of Amsterdam, The Netherlands


We thank Srinivasan Arunachalam for many helpful comments, Min-Hsiu Hsieh for sending us an updated version of [Ximing Wang et al., 2021] and answering some questions about this paper, Yassine Hamoudi for answering a question about [Yassine Hamoudi et al., 2020], and the anonymous referees for some helpful pointers.

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Adam Izdebski and Ronald de Wolf. Improved Quantum Boosting. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 64:1-64:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Boosting is a general method to convert a weak learner (which generates hypotheses that are just slightly better than random) into a strong learner (which generates hypotheses that are much better than random). Recently, Arunachalam and Maity [Srinivasan Arunachalam and Reevu Maity, 2020] gave the first quantum improvement for boosting, by combining Freund and Schapire’s AdaBoost algorithm with a quantum algorithm for approximate counting. Their booster is faster than classical boosting as a function of the VC-dimension of the weak learner’s hypothesis class, but worse as a function of the quality of the weak learner. In this paper we give a substantially faster and simpler quantum boosting algorithm, based on Servedio’s SmoothBoost algorithm [Servedio, 2003].

Subject Classification

ACM Subject Classification
  • Theory of computation → Quantum computation theory
  • Computing methodologies → Machine learning
  • Learning theory
  • Boosting algorithms
  • Quantum computing


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